def testUnknownInputChannelsConv3D(self):
volumes = array_ops.placeholder(dtypes.float32, (5, 6, 7, 9, None))
layer = conv_layers.Conv3D(32, [3, 3, 3], activation=nn_ops.relu)
with self.assertRaisesRegexp(ValueError,
'The channel dimension of the inputs '
'should be defined. Found `None`.'):
_ = layer.apply(volumes)
def testConstraints(self):
# Conv1D
k_constraint = lambda x: x / math_ops.reduce_sum(x)
b_constraint = lambda x: x / math_ops.reduce_max(x)
conv1d = conv_layers.Conv1D(2, 3,
kernel_constraint=k_constraint,
bias_constraint=b_constraint)
inputs = random_ops.random_uniform((5, 3, 5), seed=1)
conv1d(inputs)
self.assertEqual(conv1d.kernel_constraint, k_constraint)
self.assertEqual(conv1d.bias_constraint, b_constraint)
# Conv2D
k_constraint = lambda x: x / math_ops.reduce_sum(x)
b_constraint = lambda x: x / math_ops.reduce_max(x)
conv2d = conv_layers.Conv2D(2, 3,
kernel_constraint=k_constraint,
bias_constraint=b_constraint)
inputs = random_ops.random_uniform((5, 3, 3, 5), seed=1)
conv2d(inputs)
self.assertEqual(conv2d.kernel_constraint, k_constraint)
self.assertEqual(conv2d.bias_constraint, b_constraint)
# Conv3D
k_constraint = lambda x: x / math_ops.reduce_sum(x)
b_constraint = lambda x: x / math_ops.reduce_max(x)
conv3d = conv_layers.Conv3D(2, 3,
kernel_constraint=k_constraint,
bias_constraint=b_constraint)
inputs = random_ops.random_uniform((5, 3, 3, 3, 5), seed=1)
conv3d(inputs)
self.assertEqual(conv3d.kernel_constraint, k_constraint)
self.assertEqual(conv3d.bias_constraint, b_constraint)
def testUnknownInputChannelsConv3D(self):
volumes = random_ops.random_uniform((5, 6, 7, 9, 9))
volumes._shape = tensor_shape.as_shape((5, 6, 7, 9, None))
layer = conv_layers.Conv3D(32, [3, 3, 3], activation=nn_ops.relu)
with self.assertRaisesRegexp(
ValueError, 'The channel dimension of the inputs '
'should be defined. Found `None`.'):
_ = layer.apply(volumes)
def testCreateConv3D(self):
depth, height, width = 6, 7, 9
volumes = random_ops.random_uniform((5, depth, height, width, 4))
layer = conv_layers.Conv3D(32, [3, 3, 3], activation=nn_ops.relu)
output = layer.apply(volumes)
self.assertEqual(output.op.name, 'conv3d/Relu')
self.assertListEqual(output.get_shape().as_list(),
[5, depth - 2, height - 2, width - 2, 32])
self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 3, 4, 32])
self.assertListEqual(layer.bias.get_shape().as_list(), [32])
